FoReaL-Decoding lets a strong reasoning model generate the first few tokens of each sentence and a weaker model complete the sentence, cutting theoretical FLOPs by 30-55% while retaining 86-100% of accuracy on four math benchmarks.
Data diversity matters for robust instruction tuning, 2023
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What makes Reasoning Models Different? Follow the Reasoning Leader for Efficient Decoding
FoReaL-Decoding lets a strong reasoning model generate the first few tokens of each sentence and a weaker model complete the sentence, cutting theoretical FLOPs by 30-55% while retaining 86-100% of accuracy on four math benchmarks.